The present invention generally relates to predictive coding of information signals, and in particular, to an encoder and a method for predictively encoding a signal, to a decoder and a method for decoding a predictively encoded signal, to a system and a method for predictively encoding a signal and for decoding a predictively encoded version of the signal and to a predictively encoded information signal. Further embodiments of the present invention relate to a predictive coding scheme with variable reset times.
A predictive encoder (transmitter) encodes signals by predicting a current value of the signal to be encoded using the previous or preceding values of the signal. This prediction or presumption is sometimes accomplished by a weighted sum of the previous values of the signal. The prediction weights or prediction coefficients are adjusted so that the difference between the predicted signal and the actual signal is minimized in a predetermined manner. The prediction coefficients, for example, are optimized with regard to the square of the prediction error. Only the differences between the predicted values and the actual values of the signal are transmitted to the decoder or receiver. These values are also referred to as residuals or prediction errors. The actual signal value can be reconstructed in the receiver by using the same predictor (for example, identical to the predictor used in the encoder) and by adding the predicted value obtained in the same manner as in the encoder to the prediction error transmitted by the encoder.
In the case of transmitting errors, i.e. if incorrectly transmitted prediction differences or errors occur, prediction will no longer be the same on the transmitter and receiver sides. Incorrect values of the decoded signal will be reconstructed due to the incorrectly transmitted prediction errors on the receiver side.
In order to obtain resynchronization or adjustment between transmitter and receiver, the prediction weights are reset to a predefined state on the transmitter and receiver sides at times equal for both sides, a process also referred to as reset.
In U.S. Pat. No. 7,386,446 B2, it is described that if an adaptive prediction algorithm controllable by a speed coefficient is started from to operate with a first adaption speed and a first adaption precision and an accompanying first prediction precision in the case that the speed coefficient has a first value and to operate with a second, compared to the first one, lower adaption speed and a second, but compared to the first one, higher precision in the case that the speed parameter has a second value, the adaption durations occurring after the reset times where the prediction errors are at first increased due to the, not yet, adapted prediction coefficients may be decreased by at first setting the speed parameter to the first value and, after a while, to a second value. After the speed parameter has again been set to the second value after a predetermined duration after the reset times, the prediction errors and thus the residuals to be transmitted are more optimized or smaller than would be possible with the first speed parameter values.
In S. Wabnik, G. Schuller, F. Kraemer: “An Error Robust Ultra Low Delay Audio Coder Using an MA Prediction Model”, ICASSP 2009, Apr. 19-24, 2009, Taipei, Taiwan, two prediction structures for predictive perceptual audio coding in the context of the Ultra Low Delay (ULD) coding scheme are described. One structure is based on the commonly used AR signal model, leading to an IIR predictor in the decoder. The other structure is based on an MA signal model, leading to an FIR predictor in the decoder.
In S. Wabnik, Gerald Schuller, J. Hirschfeld, U. Kraemer: “Packet Loss Concealment in Predictive Audio Coding”, 2005 IEEE Workshop on Applications of Signal Processing Audio and Acoustics, Mohonk Mountain House, New Paltz, N.Y., Oct. 16-19, 2005, several concealment strategies for packet losses in the context of a low delay predictive audio coder are described.
In order to facilitate the understanding of predictive coding of information signals, reference is also made to the following documents:
J. Makhoul. Linear Prediction: A Tutorial Review, PROCEEDINGS OF THE IEEE, Vol. 63, NO. 4, April 1975; Ali H. Sayed: “Fundamentals of Adaptive Filtering”, Wiley-IEEE Press, 2003; and Simon S. Haykin, “Adaptive Filter Theory”, Prentice Hall International, 2001.
However, a general problem of know solutions is that because of such resets, the prediction errors will increase at the reset times. A larger prediction error, in turn, results in an increased necessitated bitrate for transmission. In the case that only a limited bitrate amount is available, such as in ‘Constant Bitrate Coding’, the signal quality will be reduced (e.g., due to distortions or noise).
Therefore, it is an object of the present invention to provide a predictive encoding and/or decoding scheme which allows for an improved tradeoff between prediction reliability, necessitated bitrate and signal quality.